-The conference starts on September 3rd evening by an informal meeting.
Jose C. Principe
Title: Quantifying Model Uncertainty for Semantic Segmentation using RKHS Operators
This talk presents our current goal of developing operators inspired by quantum theory to quantify
uncertainty in the outputs of machine learning models, specifically semantic segmentation. The basic
observation is that data projected to a Reproducing Kernel Hilbert Space (RKHS) with kernels built from
the expected value operator are statistical embeddings of the input data. At the same time, the RKHS
functionals obey the properties of a potential field. Therefore, one can directly apply the Schrodinger
equation to the projected data and interpret its Hermite expansion in terms of modal decompositions of the
PDF over the space of samples that express multi scale uncertainty. This methodology is quite general and
can be used in many different applications as demonstrated in the talk.
Title: Towards a Dynamic Value of Information Theory
The value of information (VoI) theory was developed in the 1960s by Ruslan Stratonovich and colleagues. Inspired by Shannon’s rate distortion theory, it defines VoI as the maximum expected utility (or the minimum expected cost) that can be achieved subject to a given information constraint. Different value functions correspond to different types of information and different optimal Markov transition probabilities. In many natural systems, such as learning and evolving systems, the information amount itself is dynamic, and here we discuss dynamical extension of the value of information theory. We formulate the corresponding variational problems defining certain geodesic curves on statistical manifolds and discuss the resulting theory. Examples for Shannon’s information and certain types of utility functions will be used for illustration. The problem of optimal control of mutation rates in evolutionary systems will be considered as an application of the theory.
Title: Diffusion capacity of single and interconnected networks
This lecture addresses the significant challenge of comprehending diffusive processes in networks in the context of complexity. Network
single and interconnected networks. Nat Commun 14, 2217 (2023).
Schieber, T., Carpi, L., Díaz-Guilera, A. et al. Quantification of network
structural dissimilarities. Nat Commun 8, 13928 (2017).
Title: Search in Imperfect Information Games
From the very dawn of the field, search with value functions was a fundamental concept of computer games research. Turing’s chess algorithm from 1950 was able to think two moves ahead, and Shannon’s work on chess from 1950 includes an extensive section on evaluation functions to be used within a search. Samuel’s checkers program from 1959 already combines search and value functions that are learned through self–play and bootstrapping. TD–Gammon improves upon those ideas and uses neural networks to learn those complex value functions — only to be again used within search. The combination of decision–time search and value functions has been present in the remarkable milestones where computers bested their human counterparts in long standing challenging games–DeepBlue for Chess and AlphaGo for Go. Until recently, this powerful framework of search aided with (learned) value functions has been limited to perfect information games. We will talk about why search matters, and about generalizing search for imperfect information games.